finelybook
PySpark Algorithms
by 作者: Mahmoud Parsian
Publisher Finelybook 出版日期: 2019
Pages: 682
Language 语言: English
Pages: EPUB
Size: 16 Mb
Book Description
This book is about PySpark: Python API for Spark. Apache Spark is an analytics engine for large-scale data processing. Spark is the open source cluster computing system that makes data analytics fast to write and fast to run. This book provides a large set of recipes for implementing big data processing and analytics using Spark and Python. The goal of this book is to show working examples in PySpark so that you can do your ETL and analytics easier. You maycut and paste examples to deliver your applicationsin PySpark.
This book introduces PySpark (Python API for Spark). You can use PySpark to tackle big datasets quickly through simple APIs in Python. You will learn how to express parallel tasks and computations with just a few lines of code,and cover applications from ETL,simple batch jobs to stream processing and machine learning.
With this book,you may dive into Spark capabilities such as RDDs (resilient distributed datasets),dataframes (data as a table of rows and columns),in-memory caching,and the interactive PySpark shell,where you may leverage Spark’s powerful built-in libraries,including Spark SQL,Spark Streaming,and MLlib.
In this book,you will learn Spark’s transformations and actions by a set of well-defined and working examples.All examples are tested and working: this means that youcan copy-cut-paste to your desired PySpark applications.Writing PySpark is much easier than writing Spark applicationsin Java and PySpark applications are not bulky at all when compared to Java Spark.
In this book you will learn:
Short introduction to Spark and PySpark
Learn about RDDs,DataFrames,SQL with worked examples
How to use important Spark transformations on RDDs (low-level APIs)
How to use SQL and DataFrame
How to read data from many different data sources and represent them as RDDs and DataFrames
Learn the power of Data Design Patterns
Learn the basics of Monoids and how you should use them in MapReduce
Learn the basics of GraphFrames for solving graph-related data problems
Implement Logistic Regression algorithms using PySpark
Basics of data partitioning and understand reduction transformations
